Object detectors are typically used to detect objects in a scene. A problem arises, however, when the relevant object detector is employed in a new environment. To address the new and changing scenery as presented in a new environment, object detectors of the prior art require typically require expert trainers or training sessions that are costly and can be time prohibitive. In some scenarios (such as a changing and mobile conflict), utilizing expert trainers may simply not be possible. Thus, a need exists for an infield training system.
The prior an for feature-based infield training is sparse. For example, there are a few online training systems available (see the List of Incorporated Literature References, Reference Nos. 2 and 3); however, they are not aimed at an end user environment. Alternatively, a few references discuss setting a region of interest with a set suppression biases. For instance, U.S. Pat. No. 7,454,388 (Literature Reference No. 6) describes using a neural network which is trained via reinforcement. However, it is most likely a slow training process and there is no mention of ease of use. Further, U.S. Pat. No. 7,272,593 (Literature Reference No. 7) requires user feedback which is time consuming and prohibitive in some scenarios. Importantly, none of the aforementioned references are suited for infield use by a non-technical person for pedestrian (object) detection.
Thus, a continuing need exists for a system that does not require user feedback and for training an object detector to be adapted to its location.